AI Is Changing Both Sides of Cybersecurity
Artificial intelligence has quickly become part of everyday business technology. Companies are using AI to analyse information, automate tasks, improve customer service, write software, and support decision-making.
The same technology can also be used by attackers.
In 2026, cybersecurity teams are dealing with a situation where AI can support both defence and attack. Security professionals can use AI to identify unusual activity, analyse large amounts of security information, and speed up certain investigations. Attackers can use similar capabilities to create more convincing messages, automate tasks, and adapt their techniques.
This does not mean every cyberattack is now powered by AI. Traditional threats such as phishing, ransomware, stolen credentials, and software vulnerabilities remain important.
The bigger change is that AI can make some existing techniques faster, easier to scale, and potentially more difficult to recognise.
AI Makes Phishing More Convincing
Phishing has been a cybersecurity problem for years, but artificial intelligence can make fraudulent communication more convincing.
Attackers can use AI-assisted tools to produce professional-looking emails, messages, and other communications without the obvious spelling and grammar mistakes that once made suspicious messages easier to identify.
AI can also help generate messages tailored to a particular person or organisation.
For example, an attacker may use publicly available information about a company, its employees, products, or current activities to create a message that appears relevant.
This makes traditional advice such as “look for spelling mistakes” less useful.
Businesses increasingly need layered protection that combines employee awareness with email security, authentication controls, monitoring, and clear procedures for reporting suspicious activity.
Social Engineering Can Become More Personalised
Social engineering attacks depend on influencing people rather than simply breaking through technical security controls.
An attacker may pretend to be a manager, supplier, customer, technical support representative, or another trusted person.
AI can help attackers prepare these interactions more efficiently.
Information from public websites, social platforms, company pages, and previous communications can potentially be combined to create more believable scenarios.
The problem is not limited to email. Similar techniques can be used through messaging platforms, phone conversations, social media, and other communication channels.
Businesses therefore need to treat identity verification as an important security process.
When a request involves money, sensitive information, credentials, or access to important systems, employees should have a reliable way to verify that the request is legitimate.
Deepfakes Create a New Identity Challenge
Generative AI has also made synthetic audio, images, and video increasingly accessible.
This creates another challenge for businesses because people have traditionally relied on seeing or hearing someone as a form of confirmation.
A convincing voice message that appears to come from an executive, for example, may create pressure on an employee to make a payment or share sensitive information.
The same principle can apply to video meetings and other forms of communication.
Businesses should therefore avoid treating a familiar voice or face as sufficient proof of identity when the requested action carries significant risk.
Verification procedures need to rely on trusted channels and established processes rather than appearance or voice alone.
AI Can Help Attackers Scale Their Operations
One of the important advantages of AI for attackers is automation.
Cybercriminal activity can involve repetitive tasks such as creating messages, analysing information, testing different approaches, or processing large amounts of data.
Automation can potentially reduce the amount of manual effort required for these activities.
This matters because cybersecurity teams already operate in an environment where they must deal with large numbers of events and potential threats.
If attackers can increase the scale of their activity while security teams remain dependent on manual processes, the workload can become difficult to manage.
This is one reason AI-assisted security tools are also becoming increasingly important for defenders.
AI Is Also Becoming a Security Tool
The story is not entirely negative.
Security teams can use AI to process large amounts of information and identify patterns that might be difficult to spot manually.
Security operations centres, for example, can receive enormous volumes of logs and alerts from endpoints, networks, cloud platforms, and identity systems.
AI-assisted tools can help organise this information, identify unusual behaviour, summarise events, and support investigations.
The technology can help analysts focus their attention on incidents that may require deeper investigation.
However, AI-generated security findings still need appropriate validation. An automated system can produce a useful signal, but human analysts remain important for understanding business context and deciding what action should be taken.
AI Can Help With Threat Detection
Traditional security systems often rely on known patterns, signatures, or predefined rules.
These methods remain useful, but attackers can change their techniques.
Machine learning and other analytical approaches can help identify unusual behaviour rather than looking only for known indicators.
For example, an organisation may have a typical pattern of employee logins, data access, and system activity. A significant deviation from that pattern could trigger additional investigation.
This type of behavioural analysis can be useful when dealing with threats that do not perfectly match previously known signatures.
It is not a replacement for traditional security controls, but it can add another layer of visibility.
AI-Generated Malware Is a Growing Concern
AI can also assist with software development, which creates concerns about how attackers might use automated coding capabilities.
Malicious software still requires technical knowledge and operational infrastructure, but AI-assisted development may lower the effort involved in certain programming tasks.
Security teams therefore need to pay attention to behaviour rather than assuming that malicious code will always look the same as older examples.
Endpoint detection, application controls, network monitoring, access restrictions, and regular patching remain important because attackers can use many different techniques to gain or maintain access.
The defensive priority should be reducing opportunities for malicious code to execute and limiting what compromised systems can access.
AI Can Increase the Speed of Vulnerability Discovery
Software vulnerabilities are another area where AI could influence cybersecurity.
Security researchers and defenders can use automated tools to analyse code, identify suspicious patterns, and assist with vulnerability research.
Attackers may attempt to use similar capabilities to discover weaknesses in applications and infrastructure.
This creates additional pressure for organisations to maintain an accurate view of their systems and keep software updated.
Knowing what applications, devices, services, and dependencies are present in an environment is an important part of managing vulnerabilities.
A business cannot easily secure a system it does not know exists.
AI Systems Can Also Become Attack Targets
There is another side to the problem that businesses sometimes overlook.
AI systems themselves can become targets.
Organisations are increasingly connecting AI applications to internal documents, databases, customer information, business workflows, and external tools.
If those systems are poorly secured, attackers may attempt to manipulate inputs, access sensitive information, abuse permissions, or exploit weaknesses in the surrounding application.
This means AI security cannot be separated completely from traditional application and infrastructure security.
Businesses need to consider what an AI system can access, what actions it can perform, and what happens if its instructions or inputs are manipulated.
Data Protection Becomes Even More Important
AI systems often depend on large amounts of information.
That information may include internal documents, customer records, business data, source code, or other sensitive material.
Sending confidential information to an AI system without understanding how that information is processed can create unnecessary risks.
Businesses should establish clear policies for what information employees and applications are allowed to provide to AI services.
Access controls, data classification, encryption, monitoring, and appropriate vendor assessment can help organisations maintain better control over sensitive information.
The goal is not to prevent employees from using AI. It is to make sure AI is used within sensible security boundaries.
Employees Need New Security Awareness
Traditional security awareness training remains useful, but AI introduces additional situations employees need to understand.
Employees may receive highly convincing messages, encounter AI-generated content, use third-party AI services, or receive requests that appear to come from senior executives.
They also need to understand how to use company-approved AI tools without accidentally exposing confidential information.
Security training should therefore evolve alongside the technology.
Employees do not need to become AI security specialists, but they should understand that familiar-looking communication is not automatically trustworthy and that sensitive information should not be entered into unknown systems.
Human Verification Still Matters
As AI-generated content becomes more convincing, businesses need reliable verification processes.
For high-risk actions, employees should have a method for confirming requests through an independent channel.
For example, a payment request received by email could require confirmation through an established internal process rather than relying on the email itself.
Similarly, changes to sensitive account information or privileged access should follow defined approval procedures.
These controls are valuable because they do not depend entirely on identifying whether a message, image, or voice recording was generated by AI.
They focus instead on verifying whether the requested action is actually authorised.
Cybersecurity Teams Need to Use AI Responsibly
Security teams can gain significant benefits from AI, but automation should be introduced carefully.
If an AI system is allowed to automatically block accounts, change configurations, or take other major actions, incorrect decisions could create operational problems.
Organisations need to define which actions can be automated and which require human approval.
AI systems should also be monitored for accuracy and performance.
The objective should be to improve security operations while maintaining appropriate human oversight, particularly for decisions that could significantly affect customers, employees, or critical business systems.
The Cybersecurity Skills Landscape Is Changing
The growth of AI in cybersecurity is also changing the skills security professionals need.
Technical knowledge of networks, operating systems, cloud platforms, identity systems, and application security remains important.
At the same time, professionals increasingly need to understand AI systems, automation, data analysis, and the risks associated with AI-enabled applications.
The ability to investigate unusual behaviour and understand how different technologies interact is becoming increasingly valuable.
Cybersecurity is not becoming less technical. In many areas, it is becoming more connected to multiple areas of technology at once.
Businesses Need an AI Security Strategy
Organisations should not wait until an AI-related security incident occurs before considering how the technology affects their risk profile.
A practical strategy can begin with simple questions.
Which AI tools are employees using?
What business information can those tools access?
Are there rules around confidential information?
Which AI systems are connected to internal applications?
What security controls protect those systems?
How would the organisation respond if an AI application were compromised?
Answering these questions can help businesses understand where AI is already being used and where additional controls may be required.
What Businesses Should Focus on in 2026
Businesses do not need to treat AI as either completely safe or inherently dangerous.
The more practical approach is to understand how the technology is being used and where it changes existing security risks.
Strong identity controls, multi-factor authentication, secure software development, employee awareness, vulnerability management, data protection, monitoring, and incident-response planning remain important.
AI can then be added as another layer for both defence and productivity.
The organisations that approach AI security thoughtfully will be better positioned to take advantage of the technology without ignoring the risks that come with it.
Final Thoughts
Artificial intelligence is changing cybersecurity because it gives both defenders and attackers new capabilities.
Attackers can potentially create more convincing social engineering campaigns, automate repetitive activities, and adapt their approaches more quickly. Security teams can use AI to analyse large datasets, identify unusual behaviour, support investigations, and automate selected defensive tasks.
The technology itself is not the complete answer.
Businesses still need strong security fundamentals, trained employees, sensible access controls, reliable monitoring, and clear processes for handling incidents.
The cybersecurity challenge in 2026 is therefore not simply about fighting AI with AI. It is about understanding where artificial intelligence changes the threat landscape and building security practices that can adapt alongside it.
